Real-time content integration based on machine learned selections

ABSTRACT

A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.

BACKGROUND

Users can execute applications on their mobile client devices to receiveposts and collections of content published by other users. For example,a user may browse content within an application and select a contentitem (e.g., slideshow, article) for viewing. When the content isrequested, the server handling the request must assemble the content,some of which may be provided by third parties, on-the-fly and send theassembled content to the user before the user notices a delay. Thelimited amount of time and limited network bandwidth constrain howcontent is selected for display.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure (FIG.) number in which that element is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network.

FIG. 2 is block diagram illustrating further details regarding amessaging server system, according to example embodiments.

FIG. 3 is a schematic diagram illustrating data which may be stored inthe database of the messaging server system, according to certainexample embodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to content (e.g., an ephemeralmessage, and associated multimedia payload of data) or a contentcollection (e.g., an ephemeral message story) may be time-limited (e.g.,made ephemeral).

FIG. 6 displays example architecture of a machine learning (ML) basedintegration engine, according to some example embodiments.

FIG. 7 shows a flow diagram of a method for integrating machine selectedcontent, according to some example embodiments.

FIG. 8 shows a flow diagram of a method for training a machineclassifier to generate offset values, according to some exampleembodiments.

FIG. 9 shows a flow diagram of a method of generating combined content,according to some example embodiments.

FIG. 10 shows an application for generating a request, according to someexample embodiments.

FIGS. 11A-C show examples of different items in a content collection,according to some example embodiments.

FIG. 12 shows an example collection of content items, according to someexample embodiments.

FIG. 13 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 14 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Assembling relevant content (e.g., movie trailers, concertnotifications, slideshows, articles) for a user to browse over a networkis challenging because the content may be in a form difficult tointegrate in response to on-the-fly user requests. For example, a moviestudio may release a trailer to an upcoming movie and try to distributethe trailer for user viewing. One approach to distributing the movietrailer would be to select a number of websites and/or web pages,publish the trailer to those sites/pages, and hope that relevant userswatch the trailer. However, such an approach may lead to poorly designedsites/pages full of content not specific to a given user. Users thatbecome annoyed with the irrelevant content may opt to avoid the site orpage altogether, thereby causing the network site or application to loseviewers, users, and/or subscribers.

The problem of irrelevant content is further exacerbated because oftencontent must be selected very quickly in response to a user requesting agiven page. For example, a web page may have a set-aside canvas areaspecially configured to display movie trailers. Modernly, the process ofselecting which movie trailer to put in the canvas area is not performeduntil a user lands on the page. However, a given user may only stay onthe page for a couple of seconds. Thus, the time to receive a requestfor content, select the content from available content, integrate thecontent into the page, and transmit the content to the user as part ofthe page must occur so fast so that (1) the user does not experiencedelay (e.g., page freeze, taking more than one second for a page toload), and/or (2) the user is still on the page when the selectedcontent is displayed. Obviously, if a content selection process is tooslow and the user navigates to another page, then the content selectionprocess is useless. Conventionally, to handle the lack-of-time issue,content may be selected far before the user requests the content (e.g.,as is the case in conventional newspapers). However, as discussed above,that approach creates the issue of irrelevant content in an onlineenvironment where users can easily navigate away from pages/applicationsbloated with irrelevant content. As is evident, the problem of selectingand integrating relevant content in a way that creates a good userexperience is difficult.

A content integration system can implement a machine learning classifiertrained on past historical user data to generate relevancy numbers thatpredict how relevant each available piece of content is to the specificuser that initiated the request. That is, a given user is paired witheach piece of content to generate the relevancy numbers using themachine classifier. In some example embodiments, the content item havingthe highest relevancy number can be used to automatically select forintegration and transmission to the user in real time (e.g., within acurrent session, while the user is on the page, within 200milliseconds).

In some example embodiments, when a user requests an aggregation ofcontent such as an ephemeral message story, as discussed in furtherdetail below with reference to FIG. 5, the high-speed selection processis triggered. The selection processes uses a machine learning classifier(e.g., random forest) to generate the relevancy values for each of theavailable online content items. The machine learning classifier can betrained on past historical data of users. The past historical user datacan include user characteristics, user browse data, subscription data,and other past user data.

The relevancy value can be generated from a swipe value and a bypassvalue, according to some example embodiments. In some exampleembodiments, the swipe value and the bypass value are added together togenerate the relevancy value. The swipe value is the likelihood that theuser that generated the request will use a swipe gesture on a piece of agiven online content item, where a swipe gesture indicates that the userwants to further examine the piece of content. The bypass value is thelikelihood that the user that generated the request will use a tapgesture on a piece of given content to skip the content and view othercontent. The machine learning classifier can take into account thecharacteristics of the user (e.g., preferences, likes, subscriptions totypes of ephemeral stories) to generate the swipe value and the bypassvalue for each of the available online content items. The machinelearning classifier can further take into account the user's browse paththat led him/her to the page that initiated the request.

In some example embodiments, once each piece of online content hasreceived a swipe value and bypass value, the online content item havingthe highest combination of swipe and bypass values (e.g., the highestrelevancy value) is selected for transmission to the user. In someexample embodiments, the entire process of generating a swipe and bypassvalues and transmitting the selected online content item to the useroccurs while the user is on the page that initiated the request (e.g.,during the current active user session). In some example embodiments, anaggregation of content (e.g., an ephemeral message story) that includesthe selected item content is generated on-the-fly in response to theuser requesting the aggregated content. Because of how the contentintegration system is configured, the entire processes of selection andtransmission of content can be performed without noticeable delay (e.g.,within 200 milliseconds of the request being generated).

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple client devices 102, each ofwhich hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via a network 106(e.g., the Internet).

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality either within the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, but to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include: message content, client device information,geolocation information, media annotation and overlays, message contentpersistence conditions, social network information, and live eventinformation, as examples. Data exchanges within the messaging system 100are invoked and controlled through functions available via userinterfaces (UIs) of the messaging client application 104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

Dealing specifically with the Application Program Interface (API) server110, this server receives and transmits message data (e.g., commands andmessage payloads) between the client device 102 and the applicationserver 112. Specifically, the Application Program Interface (API) server110 provides a set of interfaces (e.g., routines and protocols) that canbe called or queried by the messaging client application 104 in order toinvoke functionality of the application server 112. The ApplicationProgram Interface (API) server 110 exposes various functions supportedby the application server 112, including account registration; loginfunctionality: the sending of messages, via the application server 112,from a particular messaging client application 104 to another messagingclient application 104; the sending of media files (e.g., images orvideo) from a messaging client application 104 to the messaging serverapplication 114, and for possible access by another messaging clientapplication 104; the setting of a collection of media data (e.g.,story); the retrieval of a list of friends of a user of a client device102; the retrieval of such collections; the retrieval of messages andcontent; the adding and deletion of friends to a social graph; thelocation of friends within a social graph; opening an application event(e.g., relating to the messaging client application 104).

The application server 112 hosts a number of applications andsubsystems, including a messaging server application 114, an imageprocessing system 116, and a social network system 122. The messagingserver application 114 implements a number of message processingtechnologies and functions, particularly related to the aggregation andother processing of content (e.g., textual and multimedia content)included in messages received from multiple instances of the messagingclient application 104. As will be described in further detail, the textand media content from multiple sources may be aggregated intocollections of content (e.g., called stories or galleries). Thesecollections are then made available, by the messaging server application114, to the messaging client application 104. Other processor- andmemory-intensive processing of data may also be performed server-side bythe messaging server application 114, in view of the hardwarerequirements for such processing.

The application server 112 also includes an image processing system 116that is dedicated to performing various image processing operations,typically with respect to images or video received within the payload ofa message at the messaging server application 114.

The social network system 122 supports various social networking networkservices, and makes these functions and services available to themessaging server application 114. To this end, the social network system122 maintains and accesses an entity graph 304 (FIG. 3) within thedatabase 120. Examples of functions and services supported by the socialnetwork system 122 include the identification of other users of themessaging system 100 with which a particular user has relationships oris “following”, and also the identification of other entities andinterests of a particular user.

As illustrated, the application server 112 also includes amachine-learning (ML)-based content integration system 150, according tosome example embodiments. The ML-based content integration system 150 isconfigured to generate relevancy scores that describe the estimatedorganic value (EOV) of available content items (e.g., movie trailers,concert notifications) to a given user. In some example embodiments, therelevancy scores include a select value and a bypass value that aregenerated by a machine learning classifier (e.g., random forest) thathas been trained on historical user data and content data. Furtherdetails of the ML-based content integration system 150 are discussedbelow with reference to FIGS. 6-12.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of some subsystems, namely an ephemeral timer system 202, acollection management system 204, and an annotation system 206.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message orcollection of messages (e.g., a SNAPCHAT Story), selectively display andenable access to messages and associated content via the messagingclient application 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image video and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text and audio) may be organized into an “eventgallery” or an “event story.” Such a collection may be made availablefor a specified time period, such as the duration of an event to whichthe content relates. For example, content relating to a music concertmay be made available as a “story” for the duration of that musicconcert. The collection management system 204 may also be responsiblefor publishing an icon that provides notification of the existence of aparticular collection to the user interface of the messaging clientapplication 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a SNAPCHAT Geofilter orfilter) to the messaging client application 104 based on a geolocationof the client device 102. In another example, the annotation system 206operatively supplies a media overlay to the messaging client application104 based on other information, such as social network information ofthe user of the client device 102. A media overlay may include audio andvisual content and visual effects. Examples of audio and visual contentinclude pictures, texts, logos, animations, and sound effects. Anexample of a visual effect includes color overlaying. The audio andvisual content or the visual effects can be applied to a media contentitem (e.g., a photo) at the client device 102. For example, the mediaoverlay can include text that can be overlaid on top of a photographtaken by the client device 102. In another example, the media overlayincludes an identification of a location overlay (e.g., Venice beach), aname of a live event, or a name of a merchant overlay (e.g., BeachCoffee House). In another example, the annotation system 206 uses thegeolocation of the client device 102 to identify a media overlay thatincludes the name of a merchant at the geolocation of the client device102. The media overlay may include other indicia associated with themerchant. The media overlays may be stored in the database 120 andaccessed through the database server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map, and upload content associated with the selectedgeolocation. The user may also specify circumstances under which aparticular media overlay should be offered to other users. Theannotation system 206 generates a media overlay that includes theuploaded content and associates the uploaded content with the selectedgeolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest bidding merchant with a corresponding geolocationfor a predefined amount of time. In some example embodiments, themachine-learning-generated relevancy values (e.g., EOV values) are addedto a merchant's bid to boost or attenuate the merchant's bid based uponwhether the relevancy value is negative or positive for a given contentitem and user pair, where the user is the user that initiated a requestfor content, for example by requesting a live story.

FIG. 3 is a schematic diagram illustrating data 300 which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata 300 could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table314. The entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, etc. Regardless of type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between entities. Such relationships maybe social, professional (e.g., work at a common corporation ororganization) interested-based, or activity-based, merely for example.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filers includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a GPS unit of the client device102. Another type of filer is a data filer, which may be selectivelypresented to a sending user by the messaging client application 104,based on other inputs or information gathered by the client device 102during the message creation process. Examples of data filters includecurrent temperature at a specific location, a current speed at which asending user is traveling, battery life for a client device 102, or thecurrent time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe entity table 302. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a SNAPCHAT Story or a gallery). The creation of aparticular collection may be initiated by a particular user (e.g., eachuser for which a record is maintained in the entity table 302). A usermay create a “personal story” in the form of a collection of contentthat has been created and sent/broadcast by that user. To this end, theuser interface of the messaging client application 104 may include anicon that is user-selectable to enable a sending user to add specificcontent to his or her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104, based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story”,which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to example embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text to be generated by a user via a        user interface of the client device 102 and that is included in        the message 400.    -   A message image payload 406: image data captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102 and that is included in the message 400.    -   A message video payload 408: video data captured by a camera        component or retrieved from a memory component of the client        device 102 and that is included in the message 400.    -   A message audio payload 410: audio data captured by a microphone        or retrieved from the memory component of the client device 102        and that is included in the message 400.    -   Message annotations 412: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to message image payload 406, message video payload        408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 406, message video payload 408,        message audio payload 410) is to be presented or made accessible        to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to content        items included in the content (e.g., a specific image within the        message image payload 406, or a specific video in the message        video payload 408).    -   A message story identifier 418: identifier values identifying        one or more content collections (e.g., “stories”) with which a        particular content item in the message image payload 406 of the        message 400 is associated. For example, multiple images within        the message image payload 406 may each be associated with        multiple content collections using identifier values.    -   A message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 308.Similarly, values within the message video payload 408 may point to datastored within a video table 310, values stored within the messageannotations 412 may point to data stored in an annotation table 312,values stored within the message story identifier 418 may point to datastored in a story table 306, and values stored within the message senderidentifier 422 and the message receiver identifier 424 may point to userrecords stored within an entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message story 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone embodiment, where the messaging client application 104 is a SNAPCHATapplication client, an ephemeral message 502 is viewable by a receivinguser for up to a maximum of 10 seconds, depending on the amount of timethat the sending user specifies using the message duration parameter506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504 (e.g., a personal SNAPCHAT Story, or anevent story). The ephemeral message story 504 has an associated storyduration parameter 508, a value of which determines a time-duration forwhich the ephemeral message story 504 is presented and accessible tousers of the messaging system 100. The story duration parameter 508, forexample, may be the duration of a music concert, where the ephemeralmessage story 504 is a collection of content pertaining to that concert.Alternatively, a user (either the owning user or a curator user) mayspecify the value for the story duration parameter 508 when performingthe setup and creation of the ephemeral message story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message story 504 may “expire”and become inaccessible within the context of the ephemeral messagestory 504, prior to the ephemeral message story 504 itself expiring interms of the story duration parameter 508. The story duration parameter508, story participation parameter 510, and message receiver identifier424 each provide input to a story timer 514, which operationallydetermines, firstly, whether a particular ephemeral message 502 of theephemeral message story 504 will be displayed to a particular receivinguser and, if so, for how long. Note that the ephemeral message story 504is also aware of the identity of the particular receiving user as aresult of the message receiver identifier 424.

Accordingly, the story timer 514 operationally controls the overalllifespan of an associated ephemeral message story 504, as well as anindividual ephemeral message 502 included in the ephemeral message story504. In one embodiment, each and every ephemeral message 502 within theephemeral message story 504 remains viewable and accessible for atime-period specified by the story duration parameter 508. In a furtherembodiment, a certain ephemeral message 502 may expire, within thecontext of ephemeral message story 504, based on a story participationparameter 510. Note that a message duration parameter 506 may stilldetermine the duration of time for which a particular ephemeral message502 is displayed to a receiving user, even within the context of theephemeral message story 504. Accordingly, the message duration parameter506 determines the duration of time that a particular ephemeral message502 is displayed to a receiving user, regardless of whether thereceiving user is viewing that ephemeral message 502 inside or outsidethe context of an ephemeral message story 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In certain use cases, a creator of a particular ephemeral message story504 may specify an indefinite story duration parameter 508. In thiscase, the expiration of the story participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message story504 will determine when the ephemeral message story 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage story 504, with a new story participation parameter 510,effectively extends the life of an ephemeral message story 504 to equalthe value of the story participation parameter 510.

Responsive to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (and, for example, specifically the messaging client application104) to cause an indicium (e.g., an icon) associated with the relevantephemeral message story 504 to no longer be displayed within a userinterface of the messaging client application 104. Similarly, when theephemeral timer system 202 determines that the message durationparameter 506 for a particular ephemeral message 502 has expired, theephemeral timer system 202 causes the messaging client application 104to no longer display an indicium (e.g., an icon or textualidentification) associated with the ephemeral message 502.

FIG. 6 shows a functional architecture for a machine learning (ML)-basedcontent integration system 150, according to some example embodiments.As illustrated, the ML-based content integration system 150 comprises arequest engine 610, a machine learning engine 620, a selection engine630, an integration engine 640, and a display engine 650. The requestengine 610 is configured to receive requests for online content. Forexample, the request engine may receive a request for a contentcollection which has place holder spots in which selected content can beintegrated. The machine learning engine 620 is configured toautomatically generate relevancy values using a model machine learnedfrom user data. The selection engine 630 is configured to select one ormore items of content using the relevancy values. The integration engine640 is configured to prepare selected content for transmission to theuser. For example, the selection engine 630 may integrate the selectedcontent into a requested content collection. The display engine 650 isconfigured to transmit a presentation (e.g., layout code) of the contentcollection to the user that requested the content collection in anactive session without noticeable delay.

FIG. 7 shows a flow diagram of a method 700 for integrating machineselected content, according to some example embodiments. At operation705, the request engine 610 receives a request for online content. Forexample, the request may be initiated in response to a user requesting acontent collection, such as an ephemeral message story 504. At operation710, the machine learning engine 620 identifies available content items.The available content items may have been submitted by third parties(e.g., movie studios) and stored in a database for later integrationinto content collections. At operation 715, the machine learning engine620 generates relevancy values. For example, the machine learning engine620 may apply its trained model (e.g., trained random forest) on eachitem of available content to produce a swipe value and a bypass valuefor each item of available content.

At operation 720, the selection engine 630 selects a particular onlinecontent item using the relevancy values. In some example embodiments,the selection engine 630 selects the online content item having thehighest relevancy value for a given user. At operation 725, theintegration engine 640 integrates the selected content item with itemsthat have been pre-selected for display. For example, a given contentcollection may be a slideshow in which each slide is an ephemeralmessage (e.g., ephemeral message 502). The ephemeral messages may bepre-selected and compiled into a content collection using the curationinterface 208 as discussed above, with reference to FIG. 2. The contentcollection may have placeholder areas between two ephemeral messagesthat can be used to insert on-the-fly content (e.g., a movie trailerhaving high relevancy scores). In some example embodiments, a contentcollection is published with multiple pre-selected ephemeral messagesand blank placeholder spots that can be filled with on-the-fly contentupon the content selection being requested. At operation 730, thedisplay engine 650 transmits a display of the requested contentcollection.

FIG. 8 shows a flow diagram of a method 800 for training a machineclassifier to generate offset values, according to some exampleembodiments. The operations of FIG. 8 may be performed as a sub-routineto operation 715, according to some example embodiments. At operation805, the machine learning engine 620 identifies training data thatincludes historical user data and historical content data. In someexample embodiments, the historical user data includes subscriptions tocontent collections of a given publisher, categorical affinitypreferences (e.g., preference for car related content, preference forcosmetics-related content, preference for American politics-relatedcontent), the period of time a given user spends viewing a content item,the user logging out after viewing a content item, the user exiting theviewing of a content collection stream (e.g., terminating an ephemeralmessage story to return to the home page of the app), whether or not agiven user installed an app (e.g., on his/her client device 102)advertised by a given content item, preferences saved in user profiles(e.g., user profile of the social networking service such as ad opt-outsettings), whether or not the given user shared the content item afterviewing it. The historical content data in the training data includescontent items that have been displayed to the past users. The contentitems may be described by metadata that describes what content categorya given item of content belongs to (e.g., car related, cosmeticsrelated, American politics related), the type or medium of the givencontent item (e.g., ephemeral message, video clip, static image),whether the given content item has links to other networkpages/websites, whether the content item invokes an operating systemcall to another application installed on client device 102 (e.g., acontent item that invokes an App Store call on iOS to show a specificapp for install), whether the content is similar to the pre-selectedcontent of a content collection. Further, the training data may includeinformation specifying whether a given past user selected or bypassed adisplayed content item.

At operation 810, the machine learning engine 620 trains a machinelearning classifier, such as an ensemble classifier, on the trainingdata. For example, the machine learning engine 620 may train a randomforest on the training data. The random forest is a collection ofdecision trees that are trained on random subsets of the training data(e.g., the historical user data and historical content data) so as toavoid over-fitting of the decision trees. The results of the decisiontrees can generate a probability, “p[swipe]”, of a given user selectinga given content item and a probability of a given user skipping thegiven content, “p[skip]”, in a voting scheme. For example, assume agiven user described by the historical user data above and given acontent item described by the historical content data above. If thereare ten trees, seven may generate a result that the given user willselect (e.g., swipe) the content, while three may predict that the givenuser will bypass (e.g., tap) on the content. In that example,p[swipe]=70% (from 7/10) and p[skip]=30% (from 3/10). In some exampleembodiments, hundreds of trees may be used in place of ten for a morerobust result. Although swipe and skipping are discussed here as anexample, one of ordinary skill in the art appreciates that other userinputs (e.g., clicking “watch movie trailer” or “skip movie trailer”)can likewise be implemented in the machine learning approach.

At operation 815, the machine learning engine 620 identifies a currentuser and a given online item content pair. For example, the current usermay be a user that requested a content collection. Further, the givenonline content item pair may be one of a plurality of content itemsavailable for integration into the content collection. At operation 820,the machine learning engine 620 implements the trained machineclassifier to generate the relevancy values (e.g., the select likelihood“p[swipe]”, and the “p[skip]” bypass likelihood).

At operation 825, the machine learning engine 620 weights the relevancyvalues against values received from the submitters of the online contentitems, according to some example embodiments. The values received fromthe respective submitters of the online content items are bids in acurrency format (e.g., $0.50). For example, a first submitter may bemovie studio “ACME” that submits a movie trailer with a cost-per-clickbid of $0.50, whereas a second submitter may be another movie studio“XYZ” that submits another movie trailer with a cost-per-click bid of$0.75. In some example embodiments, the relevancy values are used toweight the bids (e.g., numerical values received from the online contentcreators) as follows:final_value=submitted_value+normalized_relevancy_value. The final_valueand the submitted_value are numbers in currency format. Thesubmitted_value is the value received from the creator of the content(e.g., a bid in currency format). The normalized relevancy value isgenerated by normalizing the relevancy values to currency format asfollows: normalized_relevancy_value=(w_swipe*p[swipe])+(w_skip*p[skip]),where w_swipe and w_skip are coefficients that are multiplied againstthe respective probabilities. One of the results of weighting the bidswith the machine generated values is that content that was has a lowerbid (e.g., the movie from ACME) may be boosted based on the randomforest indicating that a given user may favor the content and select it,thereby ensuring a favorable user experience.

In some example embodiments, the machine learning engine 620 generatesw_swipe and w_skip coefficients by analyzing the empirical distributionof the generated “p[swipe]” “p[skip]” likelihoods and solving the systemof equations w_swipe and w_skip such that:submitted_value*0.20=normalized_relevancy_value; and|w_swipe|=10×|w_skip|.

In this way, w_swipe and w_skip are generally set so that the normalizedrelevancy value is −20% of the submitted value. Generally, for mostitems of content, the resulting normalized relevancy value is negativedue to most users historically choosing to skip content (or not selectthe content). However, for some items of content, the normalizedrelevancy value may be positive where the content has a low skip rateand high swipe rate.

At operation 830, the machine learning engine 620 determines whetherthere are additional content items that have not received relevancyvalues or weighted relevancy values (e.g., boosted/attenuated bids). Ifthere are additional items of content, the method 800 may loop back tooperation 815 where the next item of content (e.g., the “given” item ofcontent for that iteration) is identified with the current user as apair for analysis. Likewise, operations 820 and 825 may similarly beperformed for the next item of content. If there are no more additionalitems (e.g., if all items have received relevancy values for a currentuser), then at operation 830 the machine learning engine 620 terminatesand returns to method 700 for further processing as discussed above.

FIG. 9 shows a flow diagram of a method 900 of generating a contentcollection, according to some example embodiments. At operation 905, theintegration engine 640 identifies a content collection includingpre-selected content and one or more placeholder areas. The pre-selectedcontent may include ephemeral messages selected using the curationinterface 208. At operation 910, the integration engine 640 identifies aplaceholder area within the content collection. As mentioned above, theplaceholder areas are blank areas designated to receive on-the-flycontent responsive to a request for a content collection. At operation915, the integration engine 640 embeds the online content selected atoperation 720 into the placeholder area. At operation 920, theintegration engine 640 stores the content collection having the selectedintegrated content to memory.

FIG. 10 shows an application 1005 for generating a request, according tosome example embodiments. As illustrated, the client device 102 has adisplay device 1000 that shows the executing application 1005. Theapplication 1005 displays posts 1030 (e.g., ephemeral message 502)published to the application server 112. In a collected content area1010, several icons link to respective content collections, including afirst content collection 1015, a second content collection 1020, and athird content collection 1025. A user 1035 can interact with theapplication 1005 through the display device 1000 through one or moreuser interactions (e.g., clicks, taps, swipes, tap-and-hold).

FIG. 11A shows the result of the user 1035 tapping on the icon ofcontent collection 1015. Responsive to the user 1035 tapping on the iconof content collection 1015, the request engine 610 sends a request tothe application server 112 requesting the content collection 1015. Therequest triggers the methods 700, 800, and 900 discussed above. Theresulting content collection (now having the selected content integratedinto a placeholder area) is returned to client device 102 and displayedon display device 1000. FIG. 11A shows an example title page 1100 of thecollected content. The user may navigate to the next page by performinga tap gesture as indicted by dotted circle 1105.

FIG. 11B shows a second page 1110 of the content collection 1015. Theuser 1035 may read the second page 1110 then navigate to the next pageby performing another tap gesture, as indicated by dotted circle 1115.

FIG. 11C shows a third page 1120, which is a movie trailer that has beenselected from available content using the above processes (e.g., methods700, 800, and 900). As illustrated, the third page 1120 may be a staticimage with an instruction 1125 that informs the user 1035 what gestureto perform to select the content. For example, as illustrated,instruction 1125 includes an arrow pointer that conveys to the user 1035to swipe up to watch the trailer of the example movie “Bytes”. If theuser 1035 wants to skip the trailer and view the next content item inthe content collection, he/she may tap anywhere on the page 1120. If theuser wants to watch the trailer, he/she may swipe up as indicated by thedotted circle with an arrow 1130.

FIG. 12 shows a content collection 1200, according to some exampleembodiments. The content collection 1200 includes pre-selected contentareas 1205, 1210, 1220, 1230, and placeholder areas 1215 and 1225. Thepre-selected content areas 1205, 1210, 1220, 1230 may be selected andassembled using the curation interface 208, and the placeholder areas1215 and 1225 may be kept blank (unused) until a request for the contentcollection is initiated by a user. When the user requests the contentcollection 1200, the methods 700, 800, and 900 are performed to select afirst item of content to integrate into placeholder area 1215. Next, thesecond highest ranking content item is integrated into placeholder area1225. Generally, the user may navigate from one item in the contentcollection to the next by tapping on the content item as indicated byarrow 1235. If the user is on the placeholder area 1215 (e.g., thirdpage 1120 as shown in FIG. 11C), the user may perform a swipe gesture toselect the content (e.g., view the trailer, install an app, etc.) asindicated by arrow 1240.

Software Architecture

FIG. 13 is a block diagram illustrating an example software architecture1306, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 13 is a non-limiting example of asoftware architecture 1306, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1306 may execute on hardwaresuch as machine 1400 of FIG. 14 that includes, among other things,processors 1410, memory/storage 1430, and I/O components 1450. Arepresentative hardware layer 1352 is illustrated and can represent, forexample, the machine 1400 of FIG. 14. The representative hardware layer1352 includes a processing unit 1354 having associated executableinstructions 1304. Executable instructions 1304 represent the executableinstructions of the software architecture 1306, including implementationof the methods, components and so forth described herein. The hardwarelayer 1352 also includes memory and/or storage modules memory 1356,which also have executable instructions 1304. The hardware layer 1352may also comprise other hardware 1358.

In the example architecture of FIG. 13, the software architecture 1306may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1306may include layers such as an operating system 1302, libraries 1320,applications 1316, and a presentation layer 1314. Operationally, theapplications 1316 and/or other components within the layers may invokeapplication programming interface (API) API calls 1308 through thesoftware stack and receive a response as in messages 1312 to the APIcalls 1308. The layers illustrated are representative in nature and notall software architectures have all layers. For example, some mobile orspecial purpose operating systems may not provide aframeworks/middleware 1318, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1302 may manage hardware resources and providecommon services. The operating system 1302 may include, for example, akernel 1322, services 1324 and drivers 1326. The kernel 1322 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1322 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1324 may provideother common services for the other software layers. The drivers 1326are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1326 include display drivers, cameradrivers. Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth, depending on thehardware configuration.

The libraries 1320 provide a common infrastructure that is used by theapplications 1316 and/or other components and/or layers. The libraries1320 provide functionality that allows other software components toperform tasks in an easier fashion than to interface directly with theunderlying operating system 1302 functionality (e.g., kernel 1322,services 1324 and/or drivers 1326). The libraries 1320 may includesystem libraries 1344 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1320 may include API libraries 1346 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia format such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphicslibraries (e.g., an OpenGL framework that may be used to render 2D and3D in a graphic content on a display), database libraries (e.g., SQLitethat may provide various relational database functions), web libraries(e.g., WebKit that may provide web browsing functionality), and thelike. The libraries 1320 may also include a wide variety of otherlibraries 1348 to provide many other APIs to the applications 1316 andother software components/modules.

The frameworks/middleware 1318 (also sometimes referred to asmiddleware) provide a higher-level common infrastructure that may beused by the applications 1316 and/or other software components/modules.For example, the frameworks/middleware 1318 may provide various graphicuser interface (GUI) functions, high-level resource management,high-level location services, and so forth. The frameworks/middleware1318 may provide a broad spectrum of other APIs that may be utilized bythe applications 1316 and/or other software components/modules, some ofwhich may be specific to a particular operating system 1302 or platform.

The applications 1316 include built-in applications 1338 and/orthird-party applications 1340. Examples of representative built-inapplications 1338 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. Third-party applications 1340 may include anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1340 may invoke the API calls 1308 provided bythe mobile operating system (such as operating system 1302) tofacilitate functionality described herein.

The applications 1316 may use built-in operating system functions (e.g.,kernel 1322, services 1324 and/or drivers 1326), libraries 1320, andframeworks/middleware 1318 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systemsinteractions with a user may occur through a presentation layer, such aspresentation layer 1314. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 14 is a block diagram illustrating components of a machine 1400,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 14 shows a diagrammatic representation of the machine1400 in the example form of a computer system, within which instructions1416 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1400 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1416 may be used to implement modules or componentsdescribed herein. The instructions 1416 transform the general,non-programmed machine 1400 into a particular machine 1400 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1400 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1400 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1400 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1416, sequentially or otherwise, that specify actions to betaken by machine 1400. Further, while only a single machine 1400 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1416 to perform any one or more of the methodologiesdiscussed herein.

The machine 1400 may include processors 1410, memory memory/storage1430, and I/O components 1450, which may be configured to communicatewith each other such as via a bus 1402. The memory/storage 1430 mayinclude a memory 1432, such as a main memory, or other memory storage,and a storage unit 1436, both accessible to the processors 1410 such asvia the bus 1402. The storage unit 1436 and memory 1432 store theinstructions 1416 embodying any one or more of the methodologies orfunctions described herein. The instructions 1416 may also reside,completely or partially, within the memory 1432, within the storage unit1436, within at least one of the processors 1410 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 1400. Accordingly, the memory 1432, thestorage unit 1436, and the memory of processors 1410 are examples ofmachine-readable media.

The I/O components 1450 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1450 that are included in a particular machine 1400 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1450 may include many other components that are not shown inFIG. 14. The I/O components 1450 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1450may include output components 1452 and input components 1454. The outputcomponents 1452 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1454 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1450 may includebiometric components 1456, motion components 1458, environmentalenvironment components 1460, or position components 1462 among a widearray of other components. For example, the biometric components 1456may include components to detect expressions (e.g., hand expressions,facial expressions, vocal expressions, body gestures, or eye tracking),measure biosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1458 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1460 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 1462 mayinclude location sensor components (e.g., a Global Position system (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1450 may include communication components 1464operable to couple the machine 1400 to a network 1480 or devices 1470via coupling 1472 and coupling 1482 respectively. For example, thecommunication components 1464 may include a network interface componentor other suitable device to interface with the network 1480. In furtherexamples, communication components 1464 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents. Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1470 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, the communication components 1464 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1464 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code. Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code. UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1464, such as, location via Internet Protocol (IP) geo-location,location via Wi-Fi® signal triangulation, location via detecting a NFCbeacon signal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine, and includes digital or analog communications signals orother intangible media to facilitate communication of such instructions.Instructions may be transmitted or received over the network using atransmission medium via a network interface device and using any one ofa number of well-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smart phones, tablets, ultra books, netbooks,laptops, multi-processor systems, microprocessor-based or programmableconsumer electronics, game consoles, set-top boxes, or any othercommunication device that a user may use to access a network.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or other typeof cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology. General Packet Radio Service (GPRS)technology. Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard setting organizations,other long range protocols, or other data transfer technology.

“EMPHEMERAL MESSAGE” in this context refers to a message that isaccessible for a time-limited duration. An ephemeral message may be atext, an image, a video, and the like. The access time for the ephemeralmessage may be set by the message sender. Alternatively, the access timemay be a default setting or a setting specified by the recipient.Regardless of the setting technique, the message is transitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, device,or other tangible media able to store instructions and data temporarilyor permanently and may include, but is not limited to, random-accessmemory (RAM), read-only memory (ROM), buffer memory, flash memory,optical media, magnetic media, cache memory, other types of storage(e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or anysuitable combination thereof. The term “machine-readable medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-readable medium” shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a “machine-readablemedium” refers to a single storage apparatus or device, as well as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. The term “machine-readable medium”excludes signals per se.

“COMPONENT” in this context refers to a device, physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, application program interfaces (APIs), or other technologiesthat provide for the partitioning or modularization of particularprocessing or control functions. Components may be combined via theirinterfaces with other components to carry out a machine process. Acomponent may be a packaged functional hardware unit designed for usewith other components and a part of a program that usually performs aparticular function of related functions. Components may constituteeither software components (e.g., code embodied on a machine-readablemedium) or hardware components. A “hardware component” is a tangibleunit capable of performing certain operations and may be configured orarranged in a certain physical manner. In various example embodiments,one or more computer systems (e.g., a standalone computer system, aclient computer system, or a server computer system) or one or morehardware components of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a Field-Programmable Gate Array (FPGA) or an ApplicationSpecific Integrated Circuit (ASIC).

A hardware component may also include programmable logic or circuitrythat is temporarily configured by software to perform certainoperations. For example, a hardware component may include softwareexecuted by a general-purpose processor or other programmable processor.Once configured by such software, hardware components become specificmachines (or specific components of a machine) uniquely tailored toperform the configured functions and are no longer general-purposeprocessors. It will be appreciated that the decision to implement ahardware component mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.

Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time. Hardwarecomponents can provide information to, and receive information from,other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information). The various operationsof example methods described herein may be performed, at leastpartially, by one or more processors that are temporarily configured(e.g., by software) or permanently configured to perform the relevantoperations.

Whether temporarily or permanently configured, such processors mayconstitute processor-implemented components that operate to perform oneor more operations or functions described herein. As used herein,“processor-implemented component” refers to a hardware componentimplemented using one or more processors. Similarly, the methodsdescribed herein may be at least partially processor-implemented, with aparticular processor or processors being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented components. Moreover,the one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an Application Program Interface (API)). Theperformance of certain of the operations may be distributed among theprocessors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented components may be located in a singlegeographic location (e.g., within a home environment, an officeenvironment, or a server farm). In other example embodiments, theprocessors or processor-implemented components may be distributed acrossa number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands”, “op codes”, “machine code”, etc.) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an Application SpecificIntegrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC)or any combination thereof. A processor may further be a multi-coreprocessor having two or more independent processors (sometimes referredto as “cores”) that may execute instructions contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright 2017, SNAP INC., All Rights Reserved.

What is claimed is:
 1. A method comprising: receiving, over a networkfrom a client device of a user, a request for online content, therequest generated from an active network session of an applicationexecuting on the client device of the user; in response to the request,identifying, in a database, a plurality of online content itemssubmitted with a plurality of bid values, the plurality of onlinecontent items submitted over the network by a plurality of additionalclient devices; automatically generating, using a machine learningclassifier, a plurality of negative relevancy values for the pluralityof online content items, the plurality of negative relevancy valuesindicating the plurality of online content items are likely to beskipped by the user in the active network session, each given negativerelevancy numerical value of a given online content item comprising aselect value for the given online content item and a skip value for thegiven online content item, the select value being a numerical value thatdescribes a likelihood that the user of the active network session willuse a first user input action to select the given online content item,the skip value being an additional numerical value that describes anadditional likelihood that the user of the active network session willuse a second user input action to bypass the given online content item,the second user input action being different than the first user inputaction, the skip value being higher than the corresponding select valuefor each of the plurality of negative relevancy values; generating, foreach of the plurality of content items, a combined value by lowering abid value from the plurality of bid values with a corresponding negativerelevancy value from the plurality of negative relevancy values;automatically selecting an online content item from the plurality ofonline content items based on the online content item having a highestcombined value; and causing, on the client device of the user, apresentation of the online content item during the active networksession.
 2. The method of claim 1, wherein the request is a request foraggregated content comprising a placeholder space and one or morepre-selected content items, and wherein the selected online content itemis integrated into the placeholder space among the one or morepre-selected content items, the pre-selected content items beingselected before the client device generates the request for theaggregated content.
 3. The method of claim 1, wherein the request foronline content is generated in response to the user navigating, withinthe application, to a page configured to receive one or more of theplurality of online content items.
 4. The method of claim 1, wherein thefirst user input action and the second user input action are gesturesreceived through the client device, and the client device is configuredto distinguish between the first user input action and the second userinput action.
 5. The method of claim 1, further comprising: identifyinghistorical user data that describes past user actions of past usersusing the application; and training the machine learning classifier onthe historical user data.
 6. The method of claim 5, wherein the pastuser actions include browse path data, subscription data, and userprofile data.
 7. The method of claim 6, wherein the browse path datadescribes a sequence of content items displayed to a past given user asthe user navigates through the content items using the application,wherein the subscription data specifies whether a past given user hassubscribed to a series of content items, and wherein the user profiledata describes user preferences of types of content.
 8. The method ofclaim 5, wherein the machine learning classifier implements a randomforest scheme.
 9. A system comprising: one or more processors of amachine; and a memory storing instructions that, when executed by theone or more processors, cause the machine to perform operationscomprising: receive, over a network from a client device of a user, arequest for online content, the request generated from an active networksession of an application executing on the client device of the user; inresponse to the request, identify, in a database, a plurality of onlinecontent items submitted with a plurality of bid values, the plurality ofonline content items submitted over the network by a plurality ofadditional client devices; automatically generate, using a machinelearning classifier, a plurality of negative relevancy values for theplurality of online content items, the plurality of negative relevancyvalues indicating the plurality of online content items are likely to beskipped by the user in the active network session, each given negativerelevancy numerical value of a given online content item comprising aselect value for the given online content item and a skip value for thegiven online content item, the select value being a numerical value thatdescribes a likelihood that the user of the active network session willuse a first user input action to select the given online content item,the skip value being an additional numerical value that describes anadditional likelihood that the user of the active network session willuse a second user input action to bypass the given online content item,the second user input action being different than the first user inputaction, the skip value being higher than the corresponding select valuefor each of the plurality of negative relevancy values; generate, foreach of the plurality of content items, a combined value by lowering abid value from the plurality of bid values with a corresponding negativerelevancy value from the plurality of negative relevancy values;automatically select an online content item from the plurality of onlinecontent items based on the online content item having a highest combinedvalue; and cause, on the client device of the user, a presentation ofthe online content item during the active network session.
 10. Thesystem of claim 9, wherein the request is a request for aggregatedcontent comprising a placeholder space and one or more pre-selectedcontent items, and wherein the selected online content item isintegrated into the placeholder space among the one or more pre-selectedcontent items, the pre-selected content items being selected before theclient device generates the request for the aggregated content.
 11. Thesystem of claim 9, wherein the request for online content is generatedin response to the user navigating, within the application, to a pageconfigured to receive one or more of the plurality of online contentitems.
 12. The system of claim 9, the operations further comprising:identify historical user data that describes past user actions of pastusers using the application, wherein the past user actions includebrowse path data, subscription data, and user profile data; and trainthe machine learning classifier on the historical user data.
 13. Thesystem of claim 12, wherein the browse path data describes a sequence ofcontent items displayed to a past given user as the user navigatesthrough the content items using the application, wherein thesubscription data specifies whether a past given user has subscribed toa series of content items, and wherein the user profile data describesuser preferences of types of content.
 14. A non-transitorymachine-readable storage device embodying instructions that, whenexecuted by a machine, cause the machine to perform operationscomprising: receive, over a network from a client device of a user, arequest for online content, the request generated from an active networksession of an application executing on the client device of the user; inresponse to the request, identify, in a database, a plurality of onlinecontent items submitted with a plurality of bid values, the plurality ofonline content items submitted over the network by a plurality ofadditional client devices; automatically generate, using a machinelearning classifier, a plurality of negative relevancy values for theplurality of online content items, the plurality of negative relevancyvalues indicating the plurality of online content items are likely to beskipped by the user in the active network session, each given negativerelevancy numerical value of a given online content item comprising aselect value for the given online content item and a skip value for thegiven online content item, the select value being a numerical value thatdescribes a likelihood that the user of the active network session willuse a first user input action to select the given online content item,the skip value being an additional numerical value that describes anadditional likelihood that the user of the active network session willuse a second user input action to bypass the given online content item,the second user input action being different than the first user inputaction, the skip value being higher than the corresponding select valuefor each of the plurality of negative relevancy values; generate, foreach of the plurality of content items, a combined value by lowering abid value from the plurality of bid values with a corresponding negativerelevancy value from the plurality of negative relevancy values;automatically select an online content item from the plurality of onlinecontent items based on the online content item having a highest combinedvalue; and cause, on the client device of the user, a presentation ofthe online content item during the active network session.
 15. Thenon-transitory machine-readable storage device of claim 14, wherein therequest is a request for aggregated content comprising a placeholderspace and one or more pre-selected content items, and wherein theselected online content item is integrated into the placeholder spaceamong the one or more pre-selected content items, the pre-selectedcontent items being selected before the client device generates therequest for the aggregated content.